27 research outputs found
A time series classifier
A time series is a sequence of data measured at successive time intervals. Time series analysis refers to all of the methods employed to understand such data, either with the purpose of explaining the underlying system producing the data or to try to predict future data points in the time series...An evolutionary algorithm is a non-deterministic method of searching a solution space, and modeled after biological evolutionary processes. A learning classifier system (LCS) is a form of evolutionary algorithm that operates on a population of mapping rules. We introduce the time series classifier TSC, a new type of LCS that allows for the modeling and prediction of time series data, derived from Wilson\u27s XCSR, an LCS designed for use with real-valued inputs. Our method works by modifying the makeup of the rules in the LCS so that they are suitable for use on a time series...We tested TSC on real-world historical stock data --Abstract, page iii
SupRB: A Supervised Rule-based Learning System for Continuous Problems
We propose the SupRB learning system, a new Pittsburgh-style learning
classifier system (LCS) for supervised learning on multi-dimensional continuous
decision problems. SupRB learns an approximation of a quality function from
examples (consisting of situations, choices and associated qualities) and is
then able to make an optimal choice as well as predict the quality of a choice
in a given situation. One area of application for SupRB is parametrization of
industrial machinery. In this field, acceptance of the recommendations of
machine learning systems is highly reliant on operators' trust. While an
essential and much-researched ingredient for that trust is prediction quality,
it seems that this alone is not enough. At least as important is a
human-understandable explanation of the reasoning behind a recommendation.
While many state-of-the-art methods such as artificial neural networks fall
short of this, LCSs such as SupRB provide human-readable rules that can be
understood very easily. The prevalent LCSs are not directly applicable to this
problem as they lack support for continuous choices. This paper lays the
foundations for SupRB and shows its general applicability on a simplified model
of an additive manufacturing problem.Comment: Submitted to the Genetic and Evolutionary Computation Conference 2020
(GECCO 2020
Improving the Scalability of XCS-Based Learning Classifier Systems
Using evolutionary intelligence and machine learning techniques, a broad
range of intelligent machines have been designed to perform different
tasks. An intelligent machine learns by perceiving its environmental status
and taking an action that maximizes its chances of success.
Human beings have the ability to apply knowledge learned from a
smaller problem to more complex, large-scale problems of the same or a
related domain, but currently the vast majority of evolutionary machine
learning techniques lack this ability. This lack of ability to apply the already
learned knowledge of a domain results in consuming more than
the necessary resources and time to solve complex, large-scale problems
of the domain. As the problem increases in size, it becomes difficult and
even sometimes impractical (if not impossible) to solve due to the needed
resources and time. Therefore, in order to scale in a problem domain, a
systemis needed that has the ability to reuse the learned knowledge of the
domain and/or encapsulate the underlying patterns in the domain.
To extract and reuse building blocks of knowledge or to encapsulate
the underlying patterns in a problem domain, a rich encoding is needed,
but the search space could then expand undesirably and cause bloat, e.g.
as in some forms of genetic programming (GP). Learning classifier systems
(LCSs) are a well-structured evolutionary computation based learning
technique that have pressures to implicitly avoid bloat, such as fitness
sharing through niche based reproduction.
The proposed thesis is that an LCS can scale to complex problems in
a domain by reusing the learnt knowledge from simpler problems of the
domain and/or encapsulating the underlying patterns in the domain. Wilsonâs
XCS is used to implement and test the proposed systems, which is a well-tested,
online learning and accuracy based LCS model. To extract the reusable building
blocks of knowledge, GP-tree like, code-fragments are introduced, which are more
than simply another representation (e.g. ternary or real-valued alphabets). This
thesis is extended to capture the underlying patterns in a problemusing a cyclic
representation. Hard problems are experimented to test the newly developed scalable
systems and compare them with benchmark techniques.
Specifically, this work develops four systems to improve the scalability
of XCS-based classifier systems. (1) Building blocks of knowledge are extracted
fromsmaller problems of a Boolean domain and reused in learning
more complex, large-scale problems in the domain, for the first time. By
utilizing the learnt knowledge from small-scale problems, the developed
XCSCFC (i.e. XCS with Code-Fragment Conditions) system readily solves
problems of a scale that existing LCS and GP approaches cannot, e.g. the
135-bitMUX problem. (2) The introduction of the code fragments in classifier
actions in XCSCFA (i.e. XCS with Code-Fragment Actions) enables the
rich representation of GP, which when couples with the divide and conquer
approach of LCS, to successfully solve various complex, overlapping
and niche imbalance Boolean problems that are difficult to solve using numeric
action based XCS. (3) The underlying patterns in a problem domain
are encapsulated in classifier rules encoded by a cyclic representation. The
developed XCSSMA system produces general solutions of any scale n for
a number of important Boolean problems, for the first time in the field of
LCS, e.g. parity problems. (4) Optimal solutions for various real-valued
problems are evolved by extending the existing real-valued XCSR system
with code-fragment actions to XCSRCFA. Exploiting the combined power
of GP and LCS techniques, XCSRCFA successfully learns various continuous
action and function approximation problems that are difficult to learn
using the base techniques.
This research work has shown that LCSs can scale to complex, largescale
problems through reusing learnt knowledge. The messy nature, disassociation of
message to condition order, masking, feature construction, and reuse of extracted
knowledge add additional abilities to the XCS family of LCSs. The ability to use
rich encoding in antecedent GP-like codefragments or consequent cyclic representation
leads to the evolution of accurate, maximally general and compact solutions in learning
various complex Boolean as well as real-valued problems. Effectively exploiting
the combined power of GP and LCS techniques, various continuous action
and function approximation problems are solved in a simple and straight
forward manner.
The analysis of the evolved rules reveals, for the first time in XCS, that
no matter how specific or general the initial classifiers are, all the optimal
classifiers are converged through the mechanism âbe specific then generalizeâ
near the final stages of evolution. Also that standard XCS does not use
all available information or all available genetic operators to evolve optimal
rules, whereas the developed code-fragment action based systems effectively use figure
and ground information during the training process.
Thiswork has created a platformto explore the reuse of learnt functionality,
not just terminal knowledge as present, which is needed to replicate human capabilities
Three-cornered coevolution learning classifier systems for classification
This thesis introduces a Three-Cornered Coevolution System that is capable of addressing classification tasks through coevolution (coadaptive evolution) where three different agents (i.e. a generation agent and two classification agents) learn and adapt to the changes of the problems without human involvement.
In existing pattern classification systems, humans usually play a major role in creating and controlling the problem domain. In particular, humans set up and tune the problemâs difficulty. A motivation of the work for this thesis is to design and develop an automatic pattern generation and classification system that can generate various sets of exemplars to be learned from and perform the classification tasks autonomously. The system should be able to automatically adjust the problemâs difficulty based on the learnersâ ability to learn (e.g. determining features in the problem that affect the learnersâ performance in order to generate various problems for classification at different levels of difficulty). Further, the system should be capable of addressing the classification tasks through coevolution (coadaptive evolution), where the participating agents learn and adapt to the changes of the problems without human participation. Ultimately, Learning Classifier System (LCS) is chosen to be implemented in the participating agents. LCS has several potential characteristics, such as interpretability, generalisation capability and variations in representation, that are suitable for the system.
The work can be broken down into three main phases. Phase 1 is to develop an automated evolvable problem generator to autonomously generate various problems for classification, Phase 2 is to develop the Two-Cornered Coevolution System for classification, and Phase 3 is to develop the Three-Cornered Coevolution System for classification.
Phase 1 is necessary in order to create a set of problem domains for classification (i.e. image-based data or artificial data) that can be generated automatically, where the difficulty levels of the problem can be adjusted and tuned.
Phase 2 is needed to investigate the generation agentâs ability to autonomously tune and adjust the problemâs difficulty based on the classification agentâs performance. Phase 2 is a standard coevolution system, where two different agents evolve to adapt to the changes of the problem. The classification agent evolves to learn various classification problems, while the generation agent evolves to tune and adjust the problemâs difficulty based on the learnerâs ability to learn.
Phase 3 is the final research goal. This phase develops a new coevolution system where three different agents evolve to adapt to the changes of the problem. Both of the classification agents evolve to learn various classification problems, while the generation agent evolves to tune and adjust the problemâs difficulty based on the classification agentsâ ability to learn. The classification agents use different styles of learning techniques (i.e. supervised or reinforcement learning techniques) to learn the problems. Based on the classification agentsâ ability (i.e. the difference in performance between the classification agents) the generation agent adjusts and creates various problems for classification at different levels of difficulty (i.e. various âhardâ problems).
The Three-Cornered Coevolution System offers a great potential for autonomous learning and provides useful insight into coevolution learning over the standard studies of pattern recognition. The system is capable of autonomously generating various problems, learning and providing insight into each learning systemâs ability by determining the problem domains where they perform relatively well. This is in contrast to humans having to determine the problem domains
An Improved Continuous-Action Extended Classifier Systems for Function Approximation
AbstractDue to their structural simplicity and superior generalization capability, Extended Classifier Systems (XCSs) are gaining popularity within the Artificial Intelligence community. In this study an improved XCS with continuous actions is introduced for function approximation purposes. The proposed XCSF uses âprediction zones,â rather than distinct âprediction values,â to enable multi-member match sets that would allow multiple rules to be evaluated per training step. It is shown that this would accelerate the training procedure and reduce the computational cost associated with the training phase. The improved XCSF is also shown to produce more accurate rules than the classical classifier system when it comes to approximating complex nonlinear functions
Development and operation of a real-time data acquisition system for the NASA, Langley Research Center Differential Absorption Lidar
The capabilities of the DIAL data acquisition system (DAS) for the remote measurement of atmospheric trace gas concentrations from ground and aircraft platforms were extended through the purchase and integration of other hardware and the implementation of improved software. An operational manual for the current system is presented. Hardware and peripheral device registers are outlined only as an aid in debugging any DAS problems which may arise